According to [1]}, applying the method presented in [2]} directly on large datasets is computationally expensive. The solution introduced in [1]} is to search on a proxy dataset which is rather small, and then transfer the learned network architecture to a large dataset. The search process is the same as [2]} where a RNN provides the architecture of the child network. The search space in this work contains generic convolutional cells that are expressed in terms of repeated motifs in various CNNs like combination of convolutional filter banks. Two types of convolutional cells are introduced with feature maps of the same dimension and half size, respectively. The final CNN architecture is a combination of these cells. The controller receives the output of previous cells and generate the next architecture of the final CNN.
